Banana is a fruit plant that is widely produced in Indonesia. Unfortunately, this plant is very susceptible to diseases, which can reduce the crop’s quality and quantity. This paper proposes disease detection in banana plants using a thermal camera. The detection is carried out using image processing techniques with multilevel thresholding methods. The image is captured using a thermal camera; then, the image is preprocessed to suit what is desired. After that, the image produced by the thermal camera is carried out by an image registration process so that the position is the same as the image taken using a digital camera. The image processing result is compared with the ground truth image obtained from a digital camera to determine the effectiveness of the proposed method. The proposed method’s effectiveness is measured using the parameters Recall, Precision, F-measure, and Accuracy. The effectiveness of the proposed method is quite effective because it produces parameter values above 80%, namely the recall value of 85.4%, the Precision of 89.35%, the F measure of 87.33%, and the accuracy of 92.8%.

This paper proposes a method of detecting air pollution in a region using image processing techniques. The image used is the infrared image that obtained using a modified digital camera by mounting the SRS filter. Image processing technique used is to utilize wavelet transformation. Pollutants are detected based on the average number of white pixels that appear on the image. This white pixel appears due to the reflection of the wavelength of the pollutant that hits the sensor on the camera. From the results of the proposed method detection is known that the highest pollution occurs in 12.00 which is the busiest traffic time and the lowest pollution occurred in 08.00 where the traffic passing through the area has not been crowded.

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Safety and comfort are needs for all human beings. Meanwhile, the crime rate is increasing. Therefore, a remote monitoring and control system is needed. This research offers a home monitoring and control system with a client-server model using NodeMCUESP-12E. The equipment design can be used to monitor the condition of the house through sensors installed in each room. Home monitoring includes motion detection using a motion sensor, detection of the condition of the house door using a magnetic sensor, and remote door locking using a solenoid. The system can be operated offline or online using an Android smart phone. The communication model used for client-server over the transport layer protocol is User Datagram Protocol (UDP), so the server can communicate simultaneously on two clients. The fastest average response time is 0.653 seconds. The communication model between a server and a cloud uses Transmission Control Protocol (TCP) so that the data sent or received by the server through the internet is more reliable. The cloud used is Firebase which has real-time database facilities and historical data. When online, sensor response time average is the fastest on an android at 3.898 seconds, response time control is the fastest average on a client at 7.157 seconds and the control response time average is the fastest on an android at 9.495 seconds. ABSTRAK: Keselamatan dan keselesaan merupakan keperluan penting untuk manusia. Sementara itu kadar jenayah pula kian meningkat. Oleh itu, kita perlu pemantauan jarak jauh dan sistem kawalan. Kajian ini merupakan sistem kawalan dan pemantauan rumah dengan model pelanggan-pelayan menggunakan NodeMCUESP-12E. Peralatan yang dibina boleh digunakan bagi mengawasi keadaan rumah melalui sensor yang dipasang dalam setiap bilik. Pemantauan rumah adalah termasuk pengesan gerakan menggunakan sensor gerakan, pengesan keadaan pintu rumah menggunakan sensor magnet, dan pengunci pintu jarak jauh menggunakan solenoid. Sistem ini boleh digunakan secara luar atau dalam talian menggunakan telefon pintar Android. Model komunikasi yang digunakan pada pelanggan-pelayan menggunakan protokol lapisan pengangkutan adalah Protokol Datagram Pengguna (UDP), oleh itu pelayan dapat berkomunikasi secara terus dengan dua pelanggan pada purata masa respon terlaju sebanyak 0.653 saat. Model komunikasi antara pelayan dan awan adalah menggunakan Protokol Kawalan Penghantaran (TCP), dengan harapan data dapat dihantar dan diterima oleh pelayan melalui internet dengan lebih kebolehpercayaan. Awan yang digunakan adalah Firebase yang mempunyai kelengkapan pengkalan data waktu nyata dan data sejarah. Melalui keadaan dalam talian, purata masa sensor bertindak balas adalah paling laju pada Android sebanyak 3.898 saat, purata kelajuan kawalan masa bertindak balas pada pelanggan adalah 7.157 saat dan purata masa kawalan tindak balas adalah paling laju pada Android sebanyak 9.495 saat.